CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue

Ting Jiang, Bing Xu, Tiejun Zhao, Sheng Li


Abstract
Emotion recognition in dialogue systems has gained attention in the field of natural language processing recent years, because it can be applied in opinion mining from public conversational data on social media. In this paper, we propose a hierarchical model to recognize emotions in the dialogue. In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN). Convolution is used to capture n-gram information and attention mechanism is used to obtain the relevant semantic information among words in the utterance. In the second layer, a GRU-based network helps to capture contextual information in the conversation. Furthermore, we discuss the effects of unidirectional and bidirectional networks. We conduct experiments on Friends dataset and EmotionPush dataset. The results show that our proposed model(CAN-GRU) and its variants achieve better performance than baselines.
Anthology ID:
2020.ccl-1.102
Volume:
Proceedings of the 19th Chinese National Conference on Computational Linguistics
Month:
October
Year:
2020
Address:
Haikou, China
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
1101–1111
Language:
English
URL:
https://www.aclweb.org/anthology/2020.ccl-1.102
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ccl-1.102.pdf